{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum Machine Learning with Amazon Braket: Binary Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [ 
    "This post details an approach taken by Aioi to build an exploratory\n",
    "quantum machine learning application using Amazon Braket. Quantum\n",
    "machine learning has been defined as \"a research area that explores the\n",
    "interplay of ideas from quantum computing and machine learning.\" Specifically, we explore how to use quantum computers to build a proof-of-principle classifier for risk assessment in a hypothetical car insurance use case.  We use a hybrid quantum-classical approach and train a so-called quantum neural network to perform binary classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  This demonstration is a result of collaboration with Aioi USA -\n",
    "subsidiary of Aioi Nissay Dowa Insurance which is a member of MS&AD\n",
    "Insurance Group Holdings - a major worldwide insurance organization\n",
    "with close ties to the Toyota group, offering Toyota Insurance in 37\n",
    "countries. Aioi USA is a full-service \"insurtech\" insurance agency\n",
    "that develops data science-based products and services for the\n",
    "transportation industry. Aioi was one of the first insurance companies\n",
    "to work with Amazon Braket.\n",
    "\n",
    "Aioi analyzes telematics data from self-driving vehicles to predict\n",
    "driving risks. The vehicles are equipped with a multitude of sensors and\n",
    "the goal is to use the sensor data to assign each vehicle a binary score\n",
    "(safe or fail) that indicates the health of the vehicle. The problem can\n",
    "be formalized computationally as a binary classification task in which\n",
    "the driving risk score is a binary label to vehicle's sensor data.\n",
    "\n",
    "To learn label assignments for each data point, classical machine learning\n",
    "techniques such as e.g., linear regression (LR) or deep learning (DL)\n",
    "can be applied. LR is a popular approach when the data-label mapping\n",
    "is described by a linear function. For large and complex data structures, DL offers a way to capture\n",
    "nonlinear behavior in data-label mapping.\n",
    "\n",
    "So, we have powerful classical methods to perform classification tasks; how can quantum computers help here?  The short answer is, we don't quite know yet. There are results ([arXiv:1204.5242](https://arxiv.org/abs/1204.5242), [arXiv:1601.07823](https://arxiv.org/abs/1601.07823) ) indicating that quantum LR algorithms applied to quantum data under specific assumptions can be exponentially faster than their classical counterparts operating on classical data. The flip side is that these quantum algorithms output a solution in the form of a quantum state which may not be immediately useful for further processing on a classical computer. On the DL front, quantum neural networks (QNNs) emerged as a potential replacement for classical neural nets ([arXiv:quant-ph/0201144](https://arxiv.org/abs/quant-ph/0201144)) . QNN designs to perform binary classification tasks were proposed recently (see e.g., [arXiv:1802.06002](https://arxiv.org/abs/1802.06002)) as well. An advantage of QNNs is that they can directly output a classical label value, though one still has to input data in the form of a quantum state. Whether or not QNNs have practical computational advantage over classical neural nets in DL task is very much an area of active research and the jury is not out yet on QNNs. This motivated us to explore how QNNs can be utilized for the driving risk\n",
    "assignment in the case of binary sensor data with an eye towards near-term hardware implementation that constraints QNN's circuit depth due to decoherence. \n",
    "\n",
    "        \n",
    "In this post we build quantum machine learning applications using [Amazon Braket](https://aws.amazon.com/braket/). To run the example applications developed here, you need access to the [Amazon Braket SDK](https://github.com/aws/amazon-braket-sdk-python). You can either install the Braket SDK locally from the [Amazon Braket GitHub repo](https://github.com/aws/amazon-braket-sdk-python) or, alternatively, create a managed notebook in the [Amazon Braket console](https://aws.amazon.com/console/). (Please note that you need an AWS account, if you would like to run this demo on one of the quantum hardware backends offered by Amazon Braket.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Setting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   Binary classification is an example of supervised machine learning. It\n",
    "requires a training data set to build a model that can be used to predict\n",
    "labels (driving risk scores). We assume that we are given a training set\n",
    "$T$ that consists of $M$ data-label pairs ${\\bf x}, {\\bf y}$\n",
    "$(T=\\{{\\bf x}_i, {\\bf y}_i\\}$,$i=1,M)$. Here, ${\\bf x}_i$ represents vehicle sensor data as a $N$-bit string\n",
    "${\\bf x}_i=\\{x_{i0},\\cdots,x_{iN-1}\\}$ ($x_{ij}=\\{0,1\\}$). A label\n",
    "${\\bf y}_i=\\{0,1\\}$ represents the driving risk score associated with ${\\bf x}_i$.\n",
    "\n",
    "Before we proceed with a quantum solution, it is instructive to recall\n",
    "the main steps of constructing a classical neural net (NN) based\n",
    "solution. A classical NN takes data ${\\bf x}$ and a set of\n",
    "parameters $\\vec{\\theta}$ (so-called weights) as an input and transforms it into an output\n",
    "label ${\\bf z}$ such that $\\hat{{\\bf y} }= f({\\bf x},\\vec{\\theta})$ where\n",
    "$f$ is determined by NN. The goal is then\n",
    "to use a training set to train the NN, i.e. to determine the values of\n",
    "$\\vec{\\theta}$ for which the discrepancy between the output labels and\n",
    "the training set labels is minimized. You achieve this by minimizing a\n",
    "suitably chosen loss function $L(\\hat{{\\bf y}},{\\bf y})$ over the NN\n",
    "parameters $\\vec{\\theta}$ using e.g., a gradient-based optimizer.\n",
    "\n",
    "To construct a quantum binary classifier we follow a similar procedure\n",
    "with a couple of modifications \n",
    "\n",
    "- We map our classical $N$-bit data $\\{{\\bf x}_i\\}$ onto $N$-qubit quantum states $\\{|\\psi_i\\rangle \\}$. For example, a classical bit string $\\{{\\bf x}_i\\}=0010$ maps onto $|\\psi_i\\rangle = |0010\\rangle$ \n",
    "\n",
    "- Instead of a classical NN we construct a QNN - a $N+1$-qubit circuit $\\mathcal{C}(\\{\\vec{\\theta}\\})$ (a sequence of elementary single- and two-qubit gates) that transforms the input states $\\{|\\psi_i\\rangle|0\\rangle \\}$ into output states $\\{|\\phi_i \\rangle \\}$ $|\\phi_i\\rangle = \\mathcal{C}|\\psi_i\\rangle $. The QNN circuit $\\mathcal{C}(\\{\\vec{\\theta}\\})$ depends on classical parameters $\\{\\vec{\\theta}\\}$ that can be adjusted to change the output $\\{|\\phi_i\\rangle \\}$\n",
    "\n",
    "- We use the $N+1$-th qubit to read out labels after the QNN acted on the input state.  Every time we run the QNN with the same input state and parameters $\\{\\vec{\\theta}\\}$, we measure in what quantum state the $N+1$-th qubit ends up ($|0\\rangle$ or $|1\\rangle$). We denote the frequency of observing the state $|0\\rangle$ ($|1\\rangle$ ) as $p_0$ ($p_1$). We define the observed label $\\hat{{\\bf y}}$ as $\\hat{{\\bf y}} = \\frac{1 - (p_0-p_1)}{2}$. (Note: in the language of quantum computing the difference $p_0-p_1$ equals the expected value of the Pauli $\\hat{Z}$ operator measured on the $N+1$-th qubit.) By definition, $p_0-p_1$ is a function of the QNN parameters $\\{\\vec{\\theta}\\}$ in the range $ [-1,1] $ and, thus, $\\hat{{\\bf y}}$ has the range $ [0,1] $ .\n",
    "\n",
    "In the training of the QNN circuit $\\mathcal{C}$ our goal is to find a set of parameters $\\{\\vec{\\theta}_o\\}$ such that for each data point in the training set $T$ the label value ${\\bf y}_i$ is close\n",
    "to $\\hat{{\\bf y}}_i$.\n",
    "To achieve this, we minimize the log loss function $L(\\{\\vec{\\theta}\\})$  defined as,\n",
    "\n",
    "$L(\\{\\vec{\\theta}\\})=-(\\sum\\limits_{i=1}^{M}{\\bf y}_i\\log(\\hat{{\\bf y}}_i)+(1-{\\bf y}_i)\\log(1-\\hat{{\\bf y}}_i))$.\n",
    "\n",
    "We use the Amazon Braket local simulator to evaluate $L(\\{\\vec{\\theta}\\})$ and a classical optimizer from $\\verb+scipy.optimize+$ to minimize it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping classical data onto quantum states. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in the implementation of a quantum binary classifier is to specify a quantum circuit that maps classical data onto quantum states. We map classical bit values \"0\" and \"1\" onto quantum states\n",
    "$|0\\rangle$ and $|1\\rangle$, respectively. By convention, the\n",
    "initial state of a qubit is always assumed to be $|0\\rangle$. If the\n",
    "input quantum state is $|1\\rangle$ then we obtain it from\n",
    "$|0\\rangle$ by applying a qubit flip gate $X$ i.e.\n",
    "$|1\\rangle = X|0\\rangle$. Similarly, a quantum circuit to prepare an\n",
    "input state, corresponding to classical data, consists of $X$\n",
    "gates acting on qubits that are in state $|1\\rangle$. For example, a\n",
    "quantum circuit to prepare $|\\psi_i\\rangle =|101\\rangle$ will consist\n",
    "of two $X$ gate acting on qubits 0 and 2. Below we provide code that\n",
    "generates a quantum circuit for preparing an arbitrary computational basis state\n",
    "$|\\psi_i\\rangle$ using Amazon Braket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Braket libraries\n",
    "from braket.circuits import Circuit\n",
    "from braket.aws import AwsDevice\n",
    "\n",
    "# A function that converts a bit string bitStr into a quantum circuit\n",
    "def bit_string_to_circuit(bitStr):  \n",
    "    circuit = Circuit()\n",
    "    for ind in range(len(bitStr)):\n",
    "        if bitStr[ind]=='1':\n",
    "            circuit.x(ind)\n",
    "            \n",
    "    return circuit\n",
    "    \n",
    "# provide a feature string to test the function above\n",
    "feature = '00101010'\n",
    "\n",
    "# print quantum circuit that prepares corresponding quantum state \n",
    "print(bit_string_to_circuit(feature))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Designing Quantum Neural Networks and Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know how to prepare input quantum states that correspond to classical data, the next step is to define and constuct a QNN circuit $\\mathcal{C}(\\{\\vec{\\theta}\\})$ that we will train to\n",
    "perform binary classification. We use the QNN design layout depicted in\n",
    "the figure below. It is has $2N+1$ classical parameters defining:\n",
    "$N$ two-qubit gates\n",
    "$XX(\\theta_k) = e^{-i\\frac{\\theta_k}{2} \\hat{X}_j\\hat{X}_{N+1}}$, $N$\n",
    "single-qubit gates $R_{y}(\\theta_m) = e^{-i\\frac{\\theta_m}{2}\\hat{Y}_j}$, and one single-qubit gate $R_{x}(\\theta) = e^{-i\\frac{\\theta}{2}\\hat{X}_N}$ acting on the $N+1$-th qubit.."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below implements this QNN, applies it to an arbitrary input state defined by a classical bit string, and measures the values of the label qubit using Amazon Braket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import standard numpy libraries and optimizers\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Braket imports\n",
    "from braket.circuits import Circuit, Gate, Instruction, circuit, Observable\n",
    "from braket.aws import AwsDevice, AwsQuantumTask\n",
    "from braket.devices import LocalSimulator\n",
    "\n",
    "# set Braket backend to local simulator (can be changed to other backends)\n",
    "device = LocalSimulator() \n",
    "\n",
    "# Quantum Neural Net from the QNN figure implemented in Braket\n",
    "# Inputs: bitStr - data bit string (e.g. '01010101')\n",
    "#         pars - array of parameters theta (see the QNN figure for more details) \n",
    "\n",
    "def QNN(bitStr,pars):\n",
    "    ## size of the quantum neural net circuit\n",
    "    nQbts = len(bitStr) + 1 # extra qubit is allocated for the label \n",
    "    \n",
    "    ## initialize the circuit\n",
    "    qnn = Circuit()\n",
    "    \n",
    "    ## add single-qubit X rotation to the label qubit, \n",
    "    ## initialize the input state to the one specified by bitStr\n",
    "    ## add single-qubit Y rotations to data qubits, \n",
    "    ## add XX gate between qubit i and the label qubit, \n",
    "    qnn.rx(nQbts-1, pars[0])\n",
    "    for ind in range(nQbts-1):\n",
    "        angles = pars[2*ind + 1:2*ind+1+2]\n",
    "        if bitStr[ind] == '1': # by default Braket sets input states to '0', \n",
    "                               # qnn.x(ind) flips qubit number ind to state |1\\ \n",
    "            qnn.x(ind)\n",
    "        qnn.ry(ind, angles[0]).xx(ind, nQbts-1, angles[1])\n",
    "    \n",
    "    ## add Z observable to the label qubit\n",
    "    observZ = Observable.Z()\n",
    "    qnn.expectation(observZ, target=[nQbts-1]) \n",
    "    \n",
    "    return qnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the QNN defined, we need to code up the loss function $L(\\{\\vec{\\theta}\\})$ that we minimize in order to train\n",
    "the QNN to perform binary classification. Below is the code that computes $L(\\{\\vec{\\theta}\\})$ using the local simulator in Amazon Braket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function that computes the label of a given feature bit sting bitStr\n",
    "\n",
    "def parity(bitStr):\n",
    "    return bitStr.count('1') % 2\n",
    "\n",
    "## Log loss function L(theta,phi) for a given training set trainSet\n",
    "## inputs: trainSet - array of feature bit strings e.g. ['0101','1110','0000'] \n",
    "##         pars - quantum neural net parameters theta (See the QNN figure)\n",
    "##         device -  Braket backend that will compute the log loss\n",
    "def loss(trainSet, pars, device):\n",
    "    loss = 0.0\n",
    "    for ind in range(np.size(trainSet)):  \n",
    "        ## run QNN on Braket device\n",
    "        task = device.run(QNN(trainSet[ind], pars), shots=0) \n",
    "        ## retrieve the run results <Z>\n",
    "        result = task.result()\n",
    "          \n",
    "        if parity(trainSet[ind])==0:\n",
    "            loss += -np.log2(1.0-0.5*(1.0-result.values[0]))\n",
    "        else:\n",
    "            loss += -np.log2(0.5*(1.0-result.values[0]))\n",
    "    print (\"Current value of the loss function: \", loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting it all together we are now ready to train our QNN circuit to reproduce binary classification of a training set $T$. For the example below, we assume that labels ${\\bf y}_i$ are generated by a Boolean function $\\hat{f}({\\bf x}_i) = (\\sum\\limits_{j=0}^{N-1}x_{ij})\\ {\\rm mod}\\ 2$. To emulate data in the training set $T$, we generated $11$ random $10$-bit strings (data) and assign them labels according to $\\hat{f}$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Training the QNN using gradient-based optimizer\n",
    "nBits = 10 # number of bits per feature\n",
    "\n",
    "## Random training set consisting of 11 10-bit features\n",
    "## Please explore other training sets\n",
    "trainSet = ['1101011010',\n",
    "            '1000110011',\n",
    "            '0101001001',\n",
    "            '0010000110',\n",
    "            '0101111010',\n",
    "            '0000100010',\n",
    "            '1001010000',\n",
    "            '1100110001',\n",
    "            '1000010001',\n",
    "            '0000111101',\n",
    "            '0000000001']\n",
    "\n",
    "## Initial assignment of QNN parameters theta and phi (random angles in [-pi,pi]) \n",
    "pars0 = 2 * np.pi * np.random.rand(2*nBits+1) - np.pi\n",
    "\n",
    "## Run minimization\n",
    "res = minimize(lambda pars: loss(trainSet, pars, device), pars0, method='BFGS', options={'disp':True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code and wait for the optimizer to converge. It outputs a message that looks like this when the optimizer finishes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Optimization terminated successfully.\n",
    "         Current function value: 0.000000\n",
    "         Iterations: 55\n",
    "         Function evaluations: 1430\n",
    "         Gradient evaluations: 65"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that our QNN circuit is designed to compute the parity of input data exactly for an appropriate choice of the parameters $\\{\\vec{\\theta}\\}$. Thus, the global minimum of the loss function using this QNN is zero. This is generally not the case in DL applications, however. Note also that $L(\\{\\vec{\\theta}\\})$ is not convex\n",
    "with respect to the parameters $\\{\\vec{\\theta}\\}$. This means that if the final value of the loss function value is not zero, the optimizer got stuck in a local minimum. Do not panic. Try running the optimizer with a\n",
    "different set of initial parameters \\verb+pars0+. You can also explore various minimization algorithms by\n",
    "specifying $\\verb+method=' '+$ in the minimize function.\n",
    "\n",
    "Calling $\\verb+res.x+$ outputs the optimal values of the parameters $\\{\\vec{\\theta}\\}$\n",
    "and you can use them to run the \"optimal\" QNN and perform binary classification on the data that is not a part of the training set. Try that and compute the mean square error of the classifier.\n",
    "\n",
    "For our 10-bit data example there are $2^{10}=1024$ possible\n",
    "10-bit strings, we chose a training set that has only 11 data points. Yet it is\n",
    "sufficiently large to train the QNN to act as a perfect\n",
    "binary classifier for all 1024 possible features. Can you demonstrate\n",
    "that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Print the predicted label values for all N-bit data points using the optimal QNN parameters res.x\n",
    "for ind in range(2**nBits):\n",
    "    data = format(ind, '0'+str(nBits)+'b')\n",
    "    task = device.run(QNN(data, res.x), shots=100)\n",
    "    result = task.result()\n",
    "    if (data in trainSet):\n",
    "        inSet = 'in the training set'\n",
    "    else:\n",
    "        inSet = 'NOT in the training set'\n",
    "    print('Feature:', data, '| QNN predicted parity: ', 0.5*(1-result.values[0]), ' | ', inSet) \n",
    "    print('---------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " As an exercise, use the optimal QNN parameters in $\\verb+res.x+$ and apply the\n",
    "resulting QNN to all 10-bit strings that are not in the training set.\n",
    "Record the mean square error between the predicted and computed label\n",
    "values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "This post explored the use case of binary classification to analyze\n",
    "binary (telematic) data by combining QNNs with Amazon Braket. The QNN binary classifier designed in this post\n",
    "requires the number of two-qubit gates that scales linearly with the\n",
    "feature size. This is advantageous for Noisy Intermediate Scale Quantum\n",
    "(NISQ) devices that are limited in the circuit depth due to noise. A\n",
    "future area of investigation for the team is to apply more complex\n",
    "feature sets, and constructing QNNs to classify them. You can download and play with the code from this post here."
   ]
  }
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